Nan Si
Impact in
-
- Traditional Chinese Medicine Analysis
- Natural Compounds in Disease Treatment
- Pharmacology top 5%
- Plant-based Medicinal Research
Papers in
-
- Traditional Chinese Medicine Analysis 43
- Natural Compounds in Disease Treatment 7
- Medical Research and Treatments 5
- Pharmacology 11
- Plant-based Medicinal Research 5
- Pharmacogenetics and Drug Metabolism 5
- Co-authors
- Baolin Bian (68 shared papers)Haiyu Zhao (62 shared papers)Hongjie Wang (34 shared papers)Jian Tao Yang (24 shared papers)Xiaolu Wei (38 shared papers)Yanyan Zhou (25 shared papers)Lingyu Han (14 shared papers)Hongjie Wang (21 shared papers)
In The Last Decade
Nan Si
70 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 88
- Complementary and alternative medicine 438
- Pharmacology 88
- Biomaterials 96
- Pharmacology 73
- Molecular Biology 265
Countries citing papers authored by Nan Si
This map shows the geographic impact of Nan Si's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nan Si with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nan Si more than expected).
Fields of papers citing papers by Nan Si
This network shows the impact of papers produced by Nan Si. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nan Si. The network helps show where Nan Si may publish in the future.
Co-authors
The 25 scholars most cited alongside Nan Si, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 72 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 64 | |
| 2 | 2021 | 63 | |
| 3 | 2012 | 60 | |
| 4 | 2017 | 57 | |
| 5 | 2016 | 37 | |
| 6 | 2017 | 36 | |
| 7 | 2018 | 35 | |
| 8 | 2013 | 34 | |
| 9 | 2018 | 34 | |
| 10 | 2019 | 34 | |
| 11 | 2023 | 32 | |
| 12 | 2014 | 30 | |
| 13 | 2015 | 29 | |
| 14 | 2022 | 29 | |
| 15 | 2014 | 28 | |
| 16 | 2019 | 28 | |
| 17 | 2016 | 27 | |
| 18 | 2021 | 26 | |
| 19 | 2015 | 25 | |
| 20 | 2012 | 23 |
About Nan Si
Nan Si is a scholar working on Complementary and alternative medicine, Pharmacology, Biomaterials, Nephrology and Molecular Biology, having authored 72 papers that have together received 1.1k indexed citations. Recurring topics across this work include Traditional Chinese Medicine Analysis (43 papers), Natural Compounds in Disease Treatment (7 papers), Phytochemistry and Bioactive Compounds (6 papers), Alzheimer's disease research and treatments (6 papers), Phytochemistry and Biological Activities (6 papers), Plant-based Medicinal Research (5 papers), Medical Research and Treatments (5 papers) and Pharmacogenetics and Drug Metabolism (5 papers). The work is most often cited by research in Complementary and alternative medicine (438 citations), Pharmacology (88 citations), Biomaterials (96 citations), Pharmacology (73 citations) and Molecular Biology (265 citations). Nan Si has collaborated with scholars based in China, Japan and Macao. Frequent co-authors include Baolin Bian, Haiyu Zhao, Hongjie Wang, Jian Tao Yang, Xiaolu Wei, Yanyan Zhou, Lingyu Han, Hongjie Wang, Wei Ren and Xinru Gu. Their work appears in journals such as Phytomedicine, Journal of Ethnopharmacology, Molecules, Biomedical Chromatography and PLoS ONE.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.